Bottom Line:
In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method".Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.Also, lab-to-lab comparability can be greatly simplified.

Background: Real-Time quantitative PCR is an important tool in research and clinical settings. Here, we describe two new approaches that broaden the scope of real-time quantitative PCR; namely, run-internal mini standard curves (RIMS) and direct real-time relative quantitative PCR (drqPCR). RIMS are an efficient alternative to traditional standard curves and provide both run-specific and target-specific estimates of PCR parameters. The drqPCR enables direct estimation of target ratios without reference to conventional control samples.

Methodology/principal findings: In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method". Specifically, we used a raw real-time PCR dataset as the basis for more than two-and-a-half million simulated quantifications with various user-defined conditions. Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.

Conclusions/significance: The obviation of referencing to control samples is attractive whenever unpaired samples are quantified. This may be in clinical and research settings; for instance, studies on chimerism, TREC quantifications, copy number variations etc. Also, lab-to-lab comparability can be greatly simplified.

pone-0011723-g001: Precision of RIMS-based α- and β- estimates.RIMS-based parameters were referenced by subtraction to the corresponding estimates of the full internal standard curves. Standard deviation (SD) of Δαs and Δβs were used to describe the precision of RIMS. Data were split according to target, C, and number of RIMS-sample replicates (1: black, 2: dark grey, 3: light grey or 4: white). The total number of RIMS estimates for each target can be determined from the specific C value and the number of RIMS replicates (m) as follows: n = (7−LOG (C))·(4!/(m!·(4−m)!))2·3. The precision of parameters of external standard curves (n = 6 in each figure) was calculated in a similar manner and is shown as broken, black horizontal lines. An asterisk indicates where RIMS demonstrated significantly better precision than external standard curves (p<0.001).

Mentions:
Initially, we sought an optimal sample composition strategy for RIMSs. The composition should minimize the errors of regression estimates of LOG ((N0)A,is) and LOG ((N0)B,is) in Eq. R.1. Hellemans et al point out the error of the slope in linear regression is reduced by expanding the range of the dilution and including more measurements points [13]. A similar principle applies to the error of regression estimates (cf Eq. S2.1 in Appendix S1). A large number of measurement points are not desirable with RIMS. However, it is deductable that predictor variable extremes reduce the error more effectively than those close to the predictor variable mean. We therefore based our strategy solely on “extreme concentrations” and used only two samples, of relative concentration C, for our RIMSs. Our next step was to determine the importance of C-size and number of replicate analyses of the two RIMS-samples for the precision of α- and β-estimates. The total of 8,694 different RIMS was constructed from the raw data of six 28-sample standard curve data sets presented in Table 1 (see Material and Methods). The precisions of the RIMS-based α- and β-estimates, compared with those of external standard curves, are presented in Figure 1. In particular, we found that precision was increased by increasing values of C. The benefit of increasing the RIMS-sample replicate number was less obvious.

pone-0011723-g001: Precision of RIMS-based α- and β- estimates.RIMS-based parameters were referenced by subtraction to the corresponding estimates of the full internal standard curves. Standard deviation (SD) of Δαs and Δβs were used to describe the precision of RIMS. Data were split according to target, C, and number of RIMS-sample replicates (1: black, 2: dark grey, 3: light grey or 4: white). The total number of RIMS estimates for each target can be determined from the specific C value and the number of RIMS replicates (m) as follows: n = (7−LOG (C))·(4!/(m!·(4−m)!))2·3. The precision of parameters of external standard curves (n = 6 in each figure) was calculated in a similar manner and is shown as broken, black horizontal lines. An asterisk indicates where RIMS demonstrated significantly better precision than external standard curves (p<0.001).

Mentions:
Initially, we sought an optimal sample composition strategy for RIMSs. The composition should minimize the errors of regression estimates of LOG ((N0)A,is) and LOG ((N0)B,is) in Eq. R.1. Hellemans et al point out the error of the slope in linear regression is reduced by expanding the range of the dilution and including more measurements points [13]. A similar principle applies to the error of regression estimates (cf Eq. S2.1 in Appendix S1). A large number of measurement points are not desirable with RIMS. However, it is deductable that predictor variable extremes reduce the error more effectively than those close to the predictor variable mean. We therefore based our strategy solely on “extreme concentrations” and used only two samples, of relative concentration C, for our RIMSs. Our next step was to determine the importance of C-size and number of replicate analyses of the two RIMS-samples for the precision of α- and β-estimates. The total of 8,694 different RIMS was constructed from the raw data of six 28-sample standard curve data sets presented in Table 1 (see Material and Methods). The precisions of the RIMS-based α- and β-estimates, compared with those of external standard curves, are presented in Figure 1. In particular, we found that precision was increased by increasing values of C. The benefit of increasing the RIMS-sample replicate number was less obvious.

Bottom Line:
In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method".Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.Also, lab-to-lab comparability can be greatly simplified.

Background: Real-Time quantitative PCR is an important tool in research and clinical settings. Here, we describe two new approaches that broaden the scope of real-time quantitative PCR; namely, run-internal mini standard curves (RIMS) and direct real-time relative quantitative PCR (drqPCR). RIMS are an efficient alternative to traditional standard curves and provide both run-specific and target-specific estimates of PCR parameters. The drqPCR enables direct estimation of target ratios without reference to conventional control samples.

Methodology/principal findings: In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the "comparative Ct method". Specifically, we used a raw real-time PCR dataset as the basis for more than two-and-a-half million simulated quantifications with various user-defined conditions. Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.

Conclusions/significance: The obviation of referencing to control samples is attractive whenever unpaired samples are quantified. This may be in clinical and research settings; for instance, studies on chimerism, TREC quantifications, copy number variations etc. Also, lab-to-lab comparability can be greatly simplified.